{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "import matplotlib.pyplot as plt\n",
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (7,7)\n",
    "offset = 0\n",
    "labels = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\",\n",
    "          \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
    "for i in range(9):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(X_train[i+offset], cmap='gray', interpolation='none')\n",
    "    plt.title(labels[y_train[i+offset]])\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
